Why don't people always use TensorFlow Lite, if it doesn't decrease the accuracy of the models?
This partly answer to question 1.
There is no general rule concerning accuracy or size of the model. It depends on the training data and the processed data.
The lightest is your model compared to the full accuracy model the less accurate it will be. I would run the lite model on test data and compare to the accuracy of the full model to get an exact measure of the difference.

Tensor flow has different options to save the "lite" model (optimized in size, latency, none and default). 

The following mostly answer question 2. 

Tensor flow lite is intented to provide the ability to use the model to on line predict only and load the model not to train the model. 

On the other hand Tensor flow is used to build (train) the model off line.

If your edge platform support any of the binding language provided for TensorFlow (javascript, java/kotlin, C++, python) you can use Tensorflow for prediction. The accuracy or speed options you might have selected to create the model will not be affected whether you use Tensor Flow or Tensor Flow Lite. Typically Tensor flow lite can be used on mobile devices (Ios, Android). There are other supported target, see this link